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Automating Analytics Processes

The Missing Element in the Modern Data Stack

Automating Analytics in the Data Stack

In recent years, the data stack has evolved to be more scalable, flexible and responsive. But there is a disconnect between available technology and organizations realizing the benefits. Our research shows the most common complaint organizations report about analytics and business intelligence technology is that it is not flexible or adaptable to change. The ever-growing volume of data and sources, cloud options and governance requirements necessitates an organization automate their data stack to achieve a scalable, flexible and responsive data stack by increasing agility, maximizing efficiency and connecting IT and line-of-business users.

Modern data stacks are inherently large. Data-driven organizations are increasingly treating the steps involved in extracting, integrating, aggregating, preparing, transforming and analyzing data as a continual process. Doing so requires an ecosystem of technologies that all function together. In our Analytics and Data Benchmark Research, organizations, on average, report working with more than five different technologies in their data and analytics stack. In addition, they are evaluating an additional seven technologies for use in the next 12 to 24 months. A stack containing this many disparate technologies must be automated to operate as a cohesive system.


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